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Do trend extraction approaches affect causality detection in climate change studies?

Xu Huang, Hossein Hassani, Mansi Ghodsi, Zinnia Mukherjee () and Rangan Gupta ()

Physica A: Statistical Mechanics and its Applications, 2017, vol. 469, issue C, 604-624

Abstract: Various scientific studies have investigated the causal link between solar activity (SS) and the earth’s temperature (GT). Results from literature indicate that both the detected structural breaks and existing trend have significant effects on the causality detection outcomes. In this paper, we make a contribution to this literature by evaluating and comparing seven trend extraction methods covering various aspects of trend extraction studies to date. In addition, we extend previous work by using Convergent Cross Mapping (CCM) - an advanced non-parametric causality detection technique to provide evidence on the effect of existing trend in global temperature on the causality detection outcome. This paper illustrates the use of a method to find the most reliable trend extraction approach for data preprocessing, as well as provides detailed analyses of the causality detection of each component by this approach to achieve a better understanding of the causal link between SS and GT. Furthermore, the corresponding CCM results indicate increasing significance of causal effect from SS to GT since 1880 to recent years, which provide solid evidences that may contribute on explaining the escalating global tendency of warming up recent decades.

Keywords: Trend extraction approaches; Causality detection; Convergent Cross Mapping; Sunspot number; Global temperature; Singular Spectrum Analysis (search for similar items in EconPapers)
Date: 2017
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Working Paper: Do Trend Extraction Approaches Affect Causality Detection in Climate Change Studies? (2016)
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